Three lenses for the same goal: AI that fits how your business actually works. Industry-specific models that respect your sector's rules. Pre-built agents for the workflows your industry already runs every day. Department-ready AI inside the tools your teams already use. All deployed inside your governance perimeter, with human oversight where it matters.
Applied AI looks different depending on where you start. Some teams need a working agent for a specific workflow. Some need models tuned to their industry's data and regulations. Some need AI inside the systems their teams already open every day. Most enterprises need a mix. We organize the practice around all three.
Pre-built autonomous agents for the workflows your sector already runs. KYC validation, claims triage, underwriting assist, covenant monitoring, and more. Each agent is mapped to a real workflow, not a marketing abstraction. Browse by vertical, deploy inside your perimeter.
ExploreModels shaped around your sector's rules, data structures, and decisions. Risk scoring for BFSI. Clinical decision support under HIPAA. Player analytics with responsible-gambling guardrails. Predictive maintenance trained on your equipment. Routing models for your specific lanes.
ExploreAI embedded in the tools your teams already use. Forecasting in Salesforce. Ticket routing in ServiceNow. ERP intelligence in Oracle. Document and email work in Microsoft 365. Adoption happens because nobody has to switch context.
ExploreWe maintain a growing library of pre-built agents for verticals where the workflows are well-defined and the regulatory boundary is clear. Today: Retail Banking, Commercial Banking, and Insurance. More verticals being added.
Reads customer data across core banking applications, flags inconsistencies, alerts the compliance officer with the specific mismatch.
Investigates alerts from the transaction monitoring system. Pulls customer history and counterparty context. Drafts an investigation narrative for analyst review.
Handles status and routine-service requests across chat, email, and voice. Pulls account data, answers from policy documents, escalates above its confidence threshold.
Intakes a disputed transaction, gathers merchant data, classifies the dispute type, routes the file with a first-draft customer response.
Pulls borrower financials from treasury or accounting systems, updates the covenant tracker, flags drift against original underwriting, routes exceptions to the relationship manager.
Runs continuous checks against loan covenants. Alerts the RM the moment a ratio threatens compliance.
Gathers entity data across registries, beneficial-ownership records, and internal systems. Produces an onboarding package the banker reviews.
Reconciles payment exceptions across the servicing ledger. Suggests corrections with citations to the source system.
Intakes a claim, pulls policy data, cross-checks for fraud signals, drafts the initial reserve, hands the file to an adjuster.
Classifies incoming claims by complexity and risk. Routes straight-through claims to auto-settle with review, complex claims to the right adjuster queue.
Pulls submissions from email and broker portals, extracts policy-relevant fields, checks eligibility rules, hands a pre-filled file to the underwriter.
Scans closed claims for subrogation opportunities the adjuster missed. Drafts the demand letter with supporting evidence.
We build custom agents. Tell us the workflow you want to automate and we scope it against your data, governance, and integration landscape. Most custom agents ship in 8 to 12 weeks.
Generic AI ships fast and fails fast in regulated environments. The models miss your compliance requirements, your data structures, and the decisions your teams make every day. Industry-specific AI starts with domain knowledge, then layers in the technology.
SOX, Basel III, HIPAA, PCI-DSS, responsible-gambling frameworks. Whatever governs your sector, the AI architecture reflects it before a single model gets trained.
Your data does not look like anyone else's. We map your sources, your schemas, your quality gaps, and your integration points.
AI in a dashboard nobody opens is worthless. We embed AI into the tools your teams already use.
Industry-specific language, patterns, and edge cases. A model that understands banking terminology catches what a general-purpose model misses.
Risk scoring models that reflect your institution's exposure profile, not a generic template. AML compliance with confidence-scored alerts that reduce false positives by up to 50%. Credit decisioning, KYC validation, claims triage, underwriting assist, and covenant monitoring with human review thresholds in the workflow. Regulatory reporting automation for Basel III, CECL, and SOX.
Clinical decision support under HIPAA with physician review gates at every critical step. NLP trained on medical terminology for chart summarization, coding assistance, and prior authorization. PHI boundaries enforced at the model level, not just the application layer. Permission-aware retrieval so the AI only surfaces records the user is authorized to see.
Player analytics that connect behavior patterns across properties. Responsible-gambling detection with human escalation triggers. Guest intelligence that drives F&B, hotel, and floor performance without exposing player data across systems. AML monitoring tuned to gaming-specific transaction patterns.
Predictive maintenance models trained on your specific equipment sensor data. Quality inspection AI with operator review for edge cases. Supply chain anomaly detection that flags bottlenecks before they hit production. IoT data ingestion at scale with retention and governance policies covering connected operations across plants, utilities, and field equipment.
Fleet telemetry and route data unified across systems. Real-time tracking, predictive maintenance, and load optimization. EV and autonomous fleet data integration. Compliance reporting for hours-of-service and freight regulations. Routing models tuned to your specific lanes and load profiles.
Records
Real Estate
Hospitality
ShippingCompanies buy AI platforms, roll them out, and watch nobody use them. The AI lives in its own dashboard, separated from the tools people open every day. Adoption rates sit in single digits. The fix is placement, not better AI. Department-ready AI shows up inside the systems your teams already have open.
AI-assisted forecasting inside Salesforce. Lead scoring that reflects your pipeline patterns, not generic benchmarks. Opportunity risk alerts that flag deals going sideways before the rep notices. Automated meeting prep that pulls context from CRM, email, and call history.
Intelligent screening workflows that surface qualified candidates faster. Bias detection in hiring pipelines. Employee retention modeling based on engagement signals. Onboarding automation that adapts to role and department.
Ticket routing and resolution agents inside ServiceNow. Automated triage that classifies, prioritizes, and routes tickets based on content and urgency. Knowledge base retrieval so agents get answers without searching. Incident pattern detection that spots systemic issues before they escalate.
Anomaly detection in reporting pipelines. Invoice processing automation. Budget variance analysis that flags outliers in real time. Regulatory reporting assistance for SOX, internal audit, and compliance review.
Process mining to identify bottlenecks and waste. Workflow automation for approvals, handoffs, and status updates. Capacity planning models based on historical throughput. Quality assurance checkpoints with AI-flagged exceptions.
Contract review acceleration. Clause extraction and risk flagging. Regulatory change monitoring with NLP. Compliance document generation with human review gates.
Whether it is an industry agent, a sector-specific model, or department-ready AI in your CRM, the deployment pattern is the same. Nothing runs on Scadea infrastructure. Every agent and every model runs inside your cloud, against your data, under your governance.
The AI lives where the work happens. No new logins. No separate dashboards. No training on unfamiliar interfaces.
The AI understands banking terminology, sales language, HR workflows, IT ticket patterns, and finance reporting structures. It is not a one-size model dropped into every department.
Each deployment has defined KPIs: tickets resolved, leads scored, invoices processed, claims triaged, time saved. You know within weeks whether it is working.
Domain-trained models. Regulatory-aware architectures. Proprietary data pipelines. Every solution runs on proven platforms.
We do not lock you into a single vendor. We build on whatever stack makes sense for your environment.
Industry agents, industry-specific AI, and department-ready AI sit inside our broader AI practice. They run on our infrastructure layer, get coordinated by our enterprise orchestration layer, and stay governed by our human-in-the-loop framework.
Explore All AI Solutions →
Take our readiness assessment. 10 minutes. Covers data and AI. You will know exactly where the gaps are, by industry and by department.
Industry-specific AI tunes models to your sector's data and regulations. Industry agents are pre-built autonomous workflows for verticals where the workflow is well-defined (today: Retail Banking, Commercial Banking, Insurance). Department-ready AI embeds AI inside the platforms your teams already use (Salesforce, ServiceNow, Oracle, Microsoft 365). Most enterprises mix all three.
Banking, Financial Services & Insurance; Healthcare; Casino Gaming & Hospitality; Manufacturing & Connected Industries; and Transportation & Mobility. Each has distinct regulatory requirements and data profiles that shape how we build.
No. Department-ready AI embeds into the platforms your teams already use. Industry agents deploy inside your existing cloud and integrate with your existing systems. Industry-specific models run on the data infrastructure you already have.
A focused engagement (single department, defined workflow, or one industry agent) takes 8 to 16 weeks from assessment through production. Multi-department or sector-wide programs run 4 to 12 months with phased rollout.
Yes. Most organizations start with the highest-ROI option, an industry agent for a clear workflow, a sector model for a known compliance gap, or a department deployment with strong sponsorship, and expand based on results.
Each deployment has defined KPIs tied to its scope. Sales: pipeline accuracy, lead quality. IT: resolution time, ticket deflection. Finance: processing speed, anomaly detection rate. Industry agents: workflow throughput and exception rate. Sector models: false-positive reduction and audit defensibility.
Yes. We build on your current stack: AWS, Azure, GCP, Databricks, Snowflake, or legacy systems. We integrate with what you have, upgrading where needed.
